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      Improving Students’ Retention Using Machine Learning: Impacts and Implications

      Preprint
      In review
      research-article
        1 ,
      ScienceOpen Preprints
      ScienceOpen
      Data Mining, Retention, Machine Learning, Neural Networks, SVM
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            Abstract

            Traditional statistical tools and qualitative techniques were employed in the literature to discover and forecast charac teristics/factors that impact student retention the most. Modeling the links between these early available indicators and a student's future status of engineering persistence can be very useful in improving student retention in engineering. For some years, machine learning approaches have been used in education to predict retention and discover factors impacting retention rates, with better outcomes since 2010. This study focuses on different machine learning techniques used in literature for improving students’ retention; we have identified various factors that might affect the students’ retention and employed SVM and Neural Networks for predicting students’ retention rates.

            Content

            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            11 June 2022
            Affiliations
            [1 ] Member of IEEE.org, Graduated from Technocrats Institute of Technology, MP, India
            Author notes
            Article
            10.14293/S2199-1006.1.SOR-.PPZMB0B.v1
            d2197ec8-0704-4de4-93c4-7c68b39cb71f

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Assessment, Evaluation & Research methods,Applied computer science
            Retention,Neural Networks, Machine Learning,SVM,Data Mining

            Comments

            1. The abstract is too short and lacked technical specification in details, I suggest re-writing.

            2. The introduction does not specify the main research problem, that this paper tried to solve. Also, the main research contribution is missing. I suggest the authors check the writing pattern of good SCIE journal papers for better understanding.

            3. Table 1 does not present any research work from 2020-2022.

            4. Fig 1 lacks detailed elaboration in the text.

            5. All the figures used, lack detailed elaboration in the text.

            6. The proposed work needs to be compared with some state-of-the-art works from 2021-2022 for a better understanding of the efficiency of the proposed work.

            2022-08-03 13:24 UTC
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